Quantifying uncertainty of machine learning methods for loss given default

URN to cite this document:: urn:nbn:de:bvb:355-epub-532787
DOI to cite this document:: 10.5283/epub.53278

Nagl, Matthias ; Nagl, Maximilian

; Rösch, Daniel

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 28 Nov 2022 16:34

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Abstract

Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting credit risk parameters, the approaches have been found to outperform standard statistical models. The quantification of prediction uncertainty is typically not analyzed in the machine learning credit risk setting. However, this is vital to the interests of risk managers and regulators ...